Propofol mediates signal transducer and activator of transcription 3 activation and crosstalk with phosphoinositide 3-kinase/AKT
Bibliographic record
Abstract
We previously demonstrated that propofol, an intravenous anesthetic with anti-oxidative properties, activated the phosphoinositide 3-kinase (PI3K)/AKT pathway to increase the expression of B cell lymphoma (Bcl)-2 and, therefore the anti-apoptotic potential on cardiomyocytes. Here, we wanted to determine if propofol can also activate the Janus kinase (JAK) 2/signal transducer and activator of transcription (STAT) 3 pathway, another branch of cardioprotective signaling. The cellular response of nuclear factor kappa B (NFκB) and STAT3 was also evaluated. Cardiac H9c2 cells were treated by propofol alone or in combination with pretreatment by inhibitors for JAK2/STAT3 or PI3K/AKT pathway. STAT3 and AKT phosphorylation, and STAT3 translocation were measured by western blotting and immunofluorescence staining, respectively. Propofol treatment significantly increased STAT3 phosphorylation at both tyrosine 705 and serine 727 residues. Sustained early phosphorylation of STAT3 was observed with 25~75 μM propofol at 10 and 30 min. Nuclear translocation of STAT3 was seen at 4 h after treatment with 50 μM propofol. In cultured H9c2 cells, we further demonstrated that propofol-induced STAT3 phosphorylation was reduced by pretreatment with PI3K/AKT pathway inhibitors wortmannin or API-2. Conversely, pretreatment with JAK2/STAT3 pathway inhibitor AG490 or stattic inhibited propofol-induced AKT phosphorylation. In addition, propofol induced NFκB p65 subunit perinuclear translocation. Inhibition or knockdown of STAT3 was associated with increased levels of the NFκB p65 subunit. Our results suggest that propofol induces an adaptive response by dual activation and crosstalk of cytoprotective PI3K/AKT and JAK2/STAT3 pathways. Rationale to apply propofol clinically as a preemptive cardioprotectant during cardiac surgery is supported by our findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".